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• W3211272801 abstract "Recent evidence has shown that structural magnetic resonance imaging (MRI) is an effective tool for Alzheimer's disease (AD) prediction and diagnosis. While traditional MRI-based diagnosis uses images acquired at a single time point, a longitudinal study is more sensitive and accurate in detecting early pathological changes of the AD. Two main difficulties arise in longitudinal MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects (i.e., different scanning time and different total number of scans); (2) the heterogeneous progressions of high-dimensional regions of interest (ROIs) in MRI. In this work, we propose a novel feature selection and estimation method which can be applied to extract features from the heterogeneous longitudinal MRI. A key ingredient of our method is the combination of smoothing splines and the $l_1$-penalty. We perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results corroborate the advantages of the proposed method for AD prediction in longitudinal studies." @default.
• W3211272801 created "2021-11-08" @default.
• W3211272801 creator A5000889358 @default.
• W3211272801 date "2019-11-22" @default.
• W3211272801 modified "2023-10-17" @default.
• W3211272801 title "High-Dimensional Smoothing Splines and Application in Alzheimer’s Disease Prediction Using Magnetic Resonance Imaging" @default.
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• W3211272801 doi "https://doi.org/10.1080/19466315.2019.1677492" @default.
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